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Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf
<p>This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software. </p> <p> </p>
BIM online learning content in Brazil
<p>We present the result of a survey that collected three types of content on the Internet on Building Information Modeling: (i) dissemination material, (ii) training courses, and (iii) tutorials, available online for open access in Brazil or abroad.</p> <p>The identified BIM content was categorized by the following fields:</p> <ul> <li> <p>NAME: title of content;</p> </li> <li> <p>LINK: url for online location;</p> </li> <li> <p>COMPONENT OF COMPETENCE: indicates whether the component of competence is conceptual (theoretical knowledge) or applied (skill) according to Succar, Scher and Williams (2013);</p> </li> <li> <p>COMPETENCE CLASSIFICATION: level of competence (domain or executive) according to the BIMe Initiative competence table;</p> </li> <li> <p>FORMAT: website, youtube channel, or podcast;</p> </li> <li> <p>CONTENT: dissemination, tutorial, online training course;</p> </li> <li> <p>TOOL: if the content has a specific focus on a software, its name is listed;</p> </li> <li> <p>HOURS: class hours (when applicable);</p> </li> <li> <p>CERTIFICATE: yes or no (when applicable) and</p> </li> <li> <p>NOTE: explanatory text.</p> </li> </ul>
APC modeling datasets of suicide in Brazil
<p>Datasets of Age-Period-Cohort analysis of suicides in Brazil, part of the PhD thesis of Pauliana Valéria Machado Galvão, supervised by Dr. Cosme Marcelo Furtado Passos da Silva</p>
Massive Health Education through Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil
<p><strong>Repository </strong></p> <p><strong>Dataset name: </strong>avasus_syphilis_trail_dataset.csv </p> <p><strong>Version:</strong> 1.0 </p> <p><strong>Dataset period: </strong>05/12/2016 - 01/14/2022 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>177732<strong> </strong></p> <p><strong>Number of Attributes: </strong>16 </p> <p><strong>Missing Values: </strong>Yes </p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c).</p> </li> </ul> <p><strong>Description:</strong> The data contained in the avasus_syphilis_trail_dataset.csv dataset (see Table 1) originate from AVASUS users who have taken a course on the “Syphilis and other STI” learning path. This dataset provides elemental data to analyze the impact and reach of the trails and the profile of their participants.</p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> <td> <p><strong>Source</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier of the user (anonymously).</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_gender</strong></p> </td> <td> <p>Gender of the user. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English: Female, Male or Uninformed)</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>user_occupation</strong></p> </td> <td> <p>User occupation</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_cnes</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the user works.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>CNES Code or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_level_attention</strong></p> </td> <td> <p>Identification of the health care network level for which the user works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.</p> <p>(In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")</p> <p>Or NaN.</p> </td> <td> <p>CNES.</p> </td> </tr> <tr> <td> <p><strong>user_region</strong></p> </td> <td> <p>Brazilian region in which the user resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). Other options: “Exterior” or “Não Informado” (In English: Outside or Not informed).</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_id</strong></p> </td> <td> <p>Unique identifier of the course performed by the avasus user.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Code list according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name of the course taken by the avasus user.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>Course name according to AVASUS.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_workload</strong></p> </td> <td> <p>Course timetable.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 120.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>course_creation_date</strong></p> </td> <td> <p>Course creation date.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_id</strong></p> </td> <td> <p>Unique identification of the enrollment carried out by the student in some course of the trail.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Randomly generated single integer.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_creation</strong></p> </td> <td> <p>Date the student registered.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_completion_date</strong></p> </td> <td> <p>Date the student completed the course.</p> </td> <td> <p>Date.</p> </td> <td> <p>“YYYY-MM-DD” or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_current_progress</strong></p> </td> <td> <p>Student progress regarding course completion.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Range from 0 to 100.</p> <br> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_text_evaluation</strong></p> </td> <td> <p>Comment made by the student about the course.</p> </td> <td> <p>Categorial. </p> </td> <td> <p>Free text or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> <tr> <td> <p><strong>enrollment_course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> <td> <p>AVASUS.</p> </td> </tr> </tbody> </table> <p><br> <br> <br> </p> <p><strong>References </strong></p> <p>[1] Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114 . </p> <p>[2] Brasil (2022b). Classificação brasileira de ocupações - CBO. Available from: http://www.mtecbo.gov.br/cbosite/pages/home.jsf . </p> <p>[3] Brasil (2022c). Cadastro nacional de estabelecimentos de saúde - CNES. Available from: http://cnes.datasus.gov.br/ .</p> <p><strong>Article: </strong>Massive Health Education with Technological Mediation: analyzes and impacts on the syphilis epidemic in Brazil</p>
A Novel Dataset of Misinformation Tweets Regarding the CoronaVac Vaccine in Brazil
<p>This dataset was built to analyze the spread of misinformation about CoronaVac in Brazil by using data from Twitter for two specific events: the approval for emergency use in adults over 18 years old (January 17, 2021) and the approval for use in children aged 6 to 17 years (January 20, 2022).</p> <p>We choose to label the original tweets with at least one retweet in the analyzed period. The manual labeling of such tweets was initially performed by two annotators with high knowledge about the dataset and the considered context. In cases in which there was no agreement between the two annotators, a third annotator was considered to define the class of the tweet. </p> <p>The final dataset contains <strong>1,010 tweets from January 17, 2021</strong>, and <strong>816 tweets from January 20, 2022</strong>.</p> <p>This dataset was originally built for a conference paper accepted at BraSNAM 2022. If you make use of the dataset, please also cite the following paper:</p> <p><em>Gabriel P. Oliveira, Beatriz F. Paiva, Ana Paula Couto da Silva, and Mirella M. Moro. Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil. In Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining </em><em>(BraSNAM 2022), 2022.</em></p> <pre><code>@inproceedings{brasnam/OliveiraPSM22, title = {Characterizing the Diffusion of Misinformation Regarding the CoronaVac Vaccine in Brazil}, author = {Gabriel P. Oliveira and Beatriz F. Paiva and Ana Paula Couto da Silva and Mirella M. Moro}, booktitle = {Proceedings of the XI Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)} year = {2022} }</code></pre>
BRAVES database Version 1.1 (REVISED): multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil.
<p>The BRAzilian Vehicular Emissions inventory Software (BRAVES) database is a multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil. We provide this database using a spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on SPECIATE database from the United States Environmental Protection Agency. We provide netCDF files with spatial resolution of 0.05x0.05 and annual emissions from 2013 to 2019. Files are divided by vehicle type (light, commercial-light, motorcycles, and heavy). We also provide the total vehicular emissions (sum of emissions from all vehicle types). The database contains emissions of 41 chemical species, such as ACET, ACROLEIN, ALD2, BENZ, BUTADIENE13, CH4, CO, CO2, ETH, ETHA, ETHY, ETOH, FORM, ISO, N2O, NAPH, NO, NO2, PAL, PCA, PCL, PEC, PFE, PK, coarse mode primary PM (PMC), PMG, PMN, unspeciated PM2.5 (PMOTHR), PNA, PNH4, PNO3, POC, PRPA, PSI, PSO4, PTI, SO2, TERP, TOL, VOC, and XYLMN. Codes from BRAVES are available by registering at <a href="https://hoinaski.prof.ufsc.br/BRAVES/">https://hoinaski.prof.ufsc.br/BRAVES/</a> and <a href="https://github.com/leohoinaski/BRAVES">https://github.com/leohoinaski/BRAVES</a>, where users can access instructions to run the database and download the input files.</p> <p>In this updated version from the first BRAVES database version, we have preserved estimates of ETOH and RCHO from CETESB. Brazil has a unique chemical signature of the chemical composition due to the biofuels (27% of gasoline is ethanol and 7% of diesel is bio-diesel). The emissions of C2H4O (ALD2), CH2O (FORM), and C3H6O (ACET) have been derived from RCHO emissions. We have used US-EPA Speciate to speciate compounds only when local emission factors of RCHO and ETOH are not available, such as in the case of motorcycles and heavy vehicles.</p> <p>We have also included emissions of PM2.5 (PMFINE), speciating coarse PM emissions from brake and tires (40%), road wear (53%), road dust resuspension (17%), and exhaust emissions (100%). Pixel center coordinates (longitude, latitude), pixel area (AREA), and pixel local time zone (LTZ) shift from UTC has been added to the netCDF files.</p> <p>This database has been currently part of the preprint currently under review for the journal ESSD (https://doi.org/10.5194/essd-2022-74).</p> <p> </p> <p>Files description:</p> <p>BRAVESdatabaseAnnual_BR_(typeEmiss)_(Vehicle Type)_(resolution)_(year).nc - Annual emissions in Brazil by vehicle type and 0.05x0.05 degree of resolution.</p> <p>Domain:</p> <p>lati = -36 #(Brazil) #lati = int(round(bound.miny)) # Initial latitude</p> <p>latf = 8 #(Brazil) #latf = int(round(bound.maxy)) # Final latitude</p> <p>loni = -76 #(Brazil) #loni = int(round(bound.minx)) # Initial longitude</p> <p>lonf = -32 #(Brazil) #lonf = int(round(bound.maxx)) # Final longitude</p> <p>deltaX = 0.05 # Grid resolution/spacing in x direction</p> <p>deltaY = 0.05 # Grig resolution/spacing in y direction</p> <p> </p> <p>typeEmiss:</p> <p>'TOTAL' = Total emissions/sum of emissions types<br> 'Exhaust' = Only exhaust emissions<br> 'non-exaust' = Only non-exhaust emissions<br> 'non-exaustMP' = Only Particulate Matter non-exhaust emissions<br> 'non-exaustMP_no_resusp'= Only Particulate Matter non-exhaust emission excluding road resuspension </p>
Annual rain erosion (R) in Brazil
<p>The erosivity data in Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF</strong> <strong>format </strong>and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>.</p> <p>Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487.</p> <p>The article was submitted for publication.</p> <p>Dados_Erosividade_BR.csv - Data used to model the models.<br> eros_cubist.tif - Erosivity image generated by the cubist model<br> eros_gbm.tif - Image of erosivity generated by the gbm model<br> eros_lm.tif - Erosivity image generated by the linear model<br> eros_rf.tif - Erosivity image generated by the random forest model</p>
Fuel loads monitoring dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fuel loads monitoring data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_____________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 30 monitored experimental plots of 1 hectare each (100×100 m). Three experimental treatments were then established: 12 plots were burned in May (Early-Dry Season – EDS), further 12 plots were burned in August (Mid-Dry Season – MDS), and 6 plots were kept as control by ensuring fire exclusion throughout the duration of the experiment. Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th.</p> <p>Measurements of fuel load were obtained from eight subplots of 0.5 × 0.5 m randomly distributed within each plot. Samples included graminoids, leaves and branches near the ground. Biomass was dried at 70°C for 48 hours, and weighed to determine total fuel load (kg. m<sup>-2</sup>) for each sample. Samples were taken before fire for all control, EDS and MDS burn plots during both field campaigns, and repeated sampling was carried out after fire for the burned plots. In 2020, all 30 monitored plots were sampled again in May and August.</p> <p>The files available include: i) a table (Fuel_load_measures.csv) that contains the measures of fuel loads for each plot; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_____________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5).</p>
Fire behavior dataset of Campos Amazônicos Fire Experiment (Amazonas, Brazil)
<p>This dataset presents fire behavior data related to the Campos Amazônicos Fire Experiment (CAFE) (Amazonas, Brazil) project. Located within a protected area within the largest enclave of tropical savanna in the Southern Amazon, CAFE comprises a careful and systematic experimental design that was conceived from the start to evaluate satellite observations and their potential and limitations in studying spatial and temporal fire dynamics in tropical savannas.</p> <p>More information about the experimental design can be found in the following publication:</p> <p>Alves, D. B.; Fidelis, A.; Pérez-Cabello, F.; Alvarado, S. T.; Conciani, D. E.; Cambraia, B. C.; Silveira, A. L. P.; Silva, T. S. F. Impact of Image Acquisition Lag-Time on Monitoring Short-Term Postfire Spectral Dynamics in Tropical Savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing v. 16, n. 3 (2022) - <a href="https://doi.org/10.1117/1.JRS.16.034507">https://doi.org/10.1117/1.JRS.16.034507</a></p> <p>_________________________________________________________________________________________________________</p> <p>August 08, 2022 - Version 1.0 includes data from 24 experimental fires carried out in 2019 (12 in Early-Dry Season - EDS; 12 in Middle-Dry Season - MDS), each corresponding to a plot of 1 hectare (100x100 meters). Controlled burning occurred during two separate field campaigns in 2019, the first between May 19th and 25th, and the second between August 22nd and 26th. The files available include: i) a table (Fire_behavior_dataset.csv) that contains the fire parameters calculated for each experimental fire performed; ii) a text file (List_of_variables.rtf), which details each variable available in the table.</p> <p>_________________________________________________________________________________________________________</p> <p>We thank the management team of the Campos Amazônicos National Park, and in particular to its Fire Brigade (squad leaders José Furtado Neto, Genaldo Júnior, Ademilton Carvalho, Simei Limoeiro, José Alexandre Medeiros, Antonio Machado and Leandro Lacerda, and on behalf of them to all other members of the brigade), who ensure safe burning of all fire experiments (SISBIO license number 67210-5). This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant numbers 2019/07357-8; 2015/06743-0); the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant numbers 154660/2018-3; 441968/2018-0; 303988/2018-5)</p>
Modelling dataset of the São Sepé River watershed, RS, Brazil
<p>This dataset provides maps (30m resolution) and time series suited for hydrological modelling of the São Sepé watershed, RS, Brazil. Reference period is from 01-01-2010 to 01-01-2020.</p> <p>This is a test.</p> <p>Details of the files can be found in the <strong><code>readme.csv</code> </strong>file</p> <ul> <li><code>saosepe_dem.zip</code> -- full-extension DEM-based raster maps;</li> <li><code>saosepe_lulc.zip</code> -- full-period and full-extension yearly-based land use and land cover raster maps;</li> <li><code>saosepe_series_daily.txt</code> -- full period time series of daily precipitation, streamflow and temperature;</li> <li><code>saosepe_series_weekly.txt</code> -- full period time series of weekly precipitation, streamflow and temperature;</li> <li><code>saosepe_series_monthly.txt</code> -- full period time series of monthly precipitation, streamflow and temperature;</li> <li>saosepe_series_yearly.txt -- full period time series of yearly precipitation, streamflow and temperature;</li> <li><code>saosepe_window_et24h.zip</code> -- full-period inspection window of Evapotranspiration raster maps;</li> <li><code>saosepe_window_lstDem.zip</code> -- full-period inspection window of DEM-corrected Land Surface Temperature raster maps;</li> <li><code>saosepe_window_ndvi.zip</code> -- full-period inspection window of Landsat 8 NDVI raster maps;</li> </ul> <p> </p>
Shool drop-out ut in Brazil: rates per city and informations about schools
<p>The dataset presented here is a combination of three databases created by INEP (Brazil), and referes to the years of 2014/2015: </p> <p>- Drop-out rates by city,</p> <p>- Questionnaires to principals about their schools,</p> <p>- Questionnaires about school structure.</p> <p>The original databases and dictionaires are avalilable here:</p> <p>http://portal.inep.gov.br/web/guest/indicadores-educacionais</p> <p>http://portal.inep.gov.br/artigo/-/asset_publisher/B4AQV9zFY7Bv/content/divulgados-os-microdados-do-sistema-nacional-de-avaliacao-da-educacao-basica/21206</p> <p> </p> <p> </p>
Soil Carbon Dynamics in Soybean Cropland and Forests in Mato Grosso, Brazil
<p>These files contain the carbon content, radiocarbon, and stable isotope data for soils collected to 2 m deep in forest and soybean cropland in Mato Grosso, Brazil. </p>
A comprehensive floristic knowledge of the largest Atlantic Forest fragment of the Fluminense Paraíba do Sul River Valley, Rio de Janeiro, Brazil
<p>The “<em>Serra da Concórdia</em>” is part of the Atlantic Forest phytogeographical domain in the Brazilian state of Rio de Janeiro and it has a predominant phytophysiognomy of Semideciduous Seasonal Forest. This region underwent intense habitat loss and fragmentation during the 19<sup>th</sup> century, due to coffee plantations and later pastures. With the decline of these activities, the areas were abandoned, triggering secondary succession. In 2002, the “<em>Parque Estadual da Serra da Concórdia</em>” was established in this region to preserve the remaining forest fragments. The updated list of vascular plants recorded in this protected area, published in the “<em>Catálogo de Plantas das Unidades de Conservação do Brasil</em>”, is presented here, along with information on richness, endemism, and conservation status.</p>
Landslides of the 2023 summer event of São Sebastião, southeastern Brazil
<p>In February 2023, anomalously heavy rainfall caused widespread landslides in the coastal city of São Sebastião (Southeastern Brazil). This report describes the first version of a landslide inventory dataset for this event. The inventory is based primarily on the analysis of aerial images with 10 cm spatial resolution acquired immediately after the event, as well as archive images from Google Earth and PlanetScope. Delimitation of the landslides relied on a comparison of the images along with the area's Digital Surface Model (DSM) and hydrography. The GIS vector dataset (shapefile and geopackage) contains 989 points representing the landslide's crowns, 1,116 polygons indicating the affected areas of landslides and 1 polygon of a debris flow.</p>
Data on scientific production on letramento informacional in Brazil: collection procedures and resulting corpus [Dataset of thesis]
<p>Dataset containing two dataset:</p> <p>1 - Description of the data collection procedures carried out for the thesis: Appropriation of the term letramento by Brazilian Library and Information Science: terminological-conceptual tensions surrounding letramento informacional by Alves (2023).</p> <p>2 - Four Spreadsheet in CSV UTF-8 containing in each of them a set of bibliographic references in partially raw data.</p>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
Historical distribution and current drivers of guppy occurrence in Brazil
<p>This data set was used in the paper "Historical distribution and current drivers of guppy occurrence in Brazil". The file consist of occurrence records of <em>Poecilia reticulata</em> in Brazil over different time periods. In order to evaluate the historical and current distribution of <em>P. reticulata,</em> we searched for occurrence records in two major data sources. We first compiled data from all the studies cited in the recent comprehensive review of studies of Brazilian stream fish assemblages (Dias et al., 2016). We performed this search by using electronic databases and search engines (i.e., Web of Knowledge, Google Scholar, and Scielo) to look for primary studies and published papers from Brazilian journals that provide occurrence records of <em>P. reticulata</em> in Brazil. We also used combinations of the following search terms, in English and Portuguese: “guppy fish”, “non-native”, “nonindigenous aquatic species”, and “<em>Poecilia reticulata”; </em>this second search provided additional published papers mentioning this species in Brazilian territory. Both the papers and supplementary information were screened in order to find the geographical coordinates of the sampling points where this species has been detected. As a third data source, we used all the available records of <em>P. reticulata</em> from the SpeciesLink website (http://splink.cria.org.br/, accessed 2016), which aggregates species occurrence data from major biological collections worldwide, including those from Brazilian institutions. We extracted from this platform all the associated informations (e.g., the location and the associated geographical coordinates; the sampling dates containing year, month, and day; and the names of researchers who composed the sampling teams). From these three sources, we created a database of the occurrence of <em>P. reticulata</em> in Brazil.</p> <p>Some records were excluded because the geographical coordinates and/or sampling dates were not provided in detail, or could not be determined directly or from information in the publication itself (Dias et al., 2016). Overall, incomplete and discarded records comprised only 0.7% (12 out of 1649 records) of our dataset. We further used the sampling dates and associated collector information to remove duplicate records from the database. By this means, multiple records of <em>P. reticulata</em> with the identical geographical coordinates were compared in terms of the sampling day, month, year, and collector(s). If all the information was identical, only one record was included. On the other hand, multiple records of <em>P. reticulata</em> from the same location but with different dates and collectors were retained in the final database. This final database was composed of 1402 records and was used to investigate the occurrence of <em>P. reticulata</em> over time.</p> <p>References</p> <p>Dias, M. S., J. Zuanon, T. B. A. Couto, M. Carvalho, L. N. Carvalho, H. M. V. Espírito-Santo, R. Frederico, R. P. Leitão, A. F. Mortati, T. H. S. Pires, G. Torrente-Vilara, J. do Vale, M. B. dos Anjos, F. P. Mendonça, & P. A. Tedesco, 2016. Trends in studies of Brazilian stream fish assemblages. Natureza & Conservação 14: 106–111.</p>
"Bom Batuque, Ilê Aiyê" percussion break at Ilê Aiyê's Beleza Negra, January 28, 2023. Senzala do Barro Preto. Salvador, Brazil.
<p>The clip shows the percussion break that took place during "Bom Batuque, Ilê Aiyê" as performed by Ilê Aiyê's annual Beleza Negra on January 28, 2023, led by Mestre Mário Pam. Video by author, Cody Case, with permission from Ilê Aiyê who possesses all videos. This fieldwork footage was funded by a Fulbright-Hays DDRA fellowship and received IRB and Brazil Ethics committee approval to record videos of public performances, including bloco authorization provided by founder and president Antônio Carlos dos Santos for research purposes. </p>
National Checklists 2017: Brazil Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Brazil collected using effechecka and geonames polygons
National Checklists 2019: Brazil Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Brazil collected using effechecka and geonames polygons
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.